Spatial-temporal changes in kelp extent in the Gulf Islands and Southern Vancouver Island: a remote sensing approach
Bibliographic record
Abstract
Local studies across the Salish Sea show variable patterns of kelp change, ranging from total losses to resilient populations as a result of processes that operate on different scales. Here, we present a high-resolution satellite imagery approach to defining changes in kelp extent from 2005 to 2019 for the Gulf Islands and Southern Vancouver Island. We further related these changes to sea surface temperature (SST) from the Landsat thermal sensor and the number of weeks under high SST from lighthouse data. The analysis showed that (i) semi-exposed kelp areas such as Race Rocks, were characterized by strong tidal currents, high salinity (31±1), low SST variability (10oC±1), and low variability of kelp extent (20-35% with relation to the max. kelp area) for all years, including the El Nino years. The exception was 2009 (not an El Nino year), where a larger kelp extent was observed (55%). For this region, SST rarely rose above 12oC, except in strong El Nino when for about four weeks SST was higher than 12oC. (ii) Protected kelp areas such as Mayne Island in the Gulf Islands, were characterized by low tidal currents, lower salinity (26±3), and warmer (18oC±3) waters, and presented high variability (1-35%) of kelp extent. Maximum kelp extent (~30%) was observed in years 2012, 2013, 2017, and 2019, generally characterized by no or weak El Nino influence. These years showed SST around 17oC (±1) and 16 weeks of the spring/summer when the ocean was warmer than 14oC. The lowest kelp areas (~1%) were observed in El Nino years, in which SST in July was 19oC (±1), and temperatures were above 14oC for about 20 weeks. This analysis highlights the complexity of defining kelp resilience in the Salish Sea, and it indicates the potential for defining kelp sentinel regions covering different environmental conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".